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Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

Author

Listed:
  • Luisa Roa
  • Alejandro Correa-Bahnsen
  • Gabriel Suarez
  • Fernando Cort'es-Tejada
  • Mar'ia A. Luque
  • Cristi'an Bravo

Abstract

In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.

Suggested Citation

  • Luisa Roa & Alejandro Correa-Bahnsen & Gabriel Suarez & Fernando Cort'es-Tejada & Mar'ia A. Luque & Cristi'an Bravo, 2020. "Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications," Papers 2005.14658, arXiv.org, revised Jan 2021.
  • Handle: RePEc:arx:papers:2005.14658
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    References listed on IDEAS

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    Cited by:

    1. Alfonso-Sánchez, Sherly & Solano, Jesús & Correa-Bahnsen, Alejandro & Sendova, Kristina P. & Bravo, Cristián, 2024. "Optimizing credit limit adjustments under adversarial goals using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 315(2), pages 802-817.
    2. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    3. Gabriel Suarez & Juan Raful & Maria A. Luque & Carlos F. Valencia & Alejandro Correa-Bahnsen, 2021. "Enhancing User' s Income Estimation with Super-App Alternative Data," Papers 2104.05831, arXiv.org, revised Aug 2021.
    4. Luisa Roa & Andr'es Rodr'iguez-Rey & Alejandro Correa-Bahnsen & Carlos Valencia, 2021. "Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data," Papers 2102.09974, arXiv.org.
    5. Gala, Kaushik, 2024. "Digital Davids, global Goliaths, and the Web3 sling," Business Horizons, Elsevier, vol. 67(1), pages 5-17.
    6. Jaime D. Acevedo-Viloria & Luisa Roa & Soji Adeshina & Cesar Charalla Olazo & Andr'es Rodr'iguez-Rey & Jose Alberto Ramos & Alejandro Correa-Bahnsen, 2021. "Relational Graph Neural Networks for Fraud Detection in a Super-App environment," Papers 2107.13673, arXiv.org, revised Jul 2021.

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